Maintenance for wind turbines: reliability analyses, health monitoring and maintenance optimization

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Delft University of Technology

FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING

Department Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

This report consists of 51 pages and 0 appendices. It may only be reproduced literally and as a whole. For commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning the contents of the advice.

Specialization: Transport Engineering and Logistics

Report number: 2017.TEL.8194.

Title:

Maintenance for wind turbines:

reliability analyses, health

monitoring and maintenance

optimization

Author:

M. Dalmijn

Title (in Dutch) Onderhoud van windturbines: betrouwbaarheidsonderzoeken, conditie controle en onderhoudsoptimalisatie.

Assignment: literature assignment

Confidential: no

Initiator (university): Dr. ir. Y. Pang

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T

U

Delft

FACULTY OF MECHANICAL, MARITIME AND

MATERIALS ENGINEERING

Delft University of Technoiogy Department of Marine and Transport Technology

Mekelweg 2 2628 CD Delft the Netheriands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

Student: Menno Dalmijn

Supervisor: Y. Pang

Specialization: TEL

Creditpoints (EC): 10

Assignment type: Literature

Report number: 2017.TEL8194

Confidential: No

Subject: Maintenance for wind turbines: reliability analyses, health monitoring and

maintenance optimization.

Maintenance plays an important role for the total operation and relative expenditures in the wind

energy sector. The trend towards larger wind turbines installed on more remote locations has put

pressure on the efficiency of maintenance programs. For this reason, much effort has been made to

reduce the costs related to operation and maintenance.

In order to develop efficient maintenance programs there is the need for accurate reliability analyses

of the concerned equipment. The statistics related to failure events and succeeding downtime

determine the most critical components and provide crucial requirements for maintenance strategies.

The analyses of failure characteristics identify the inspection methods that can be used to monitor the

health state of the components as well as the overall equipment. Nowadays, most wind turbines are

equipped with systems that can monitor the condition of some of the critical components. These

systems can be used to provide the necessary information for condition-based maintenance strategies

as they enable the diagnosis and prognosis of faults and failures. Further appropriate maintenance

interventions can be determined to reduce the overall operational costs. This literature assignment is

aimed to provide an overview of the maintenance of wind turbines. The survey of this assignment

should cover the following:

• To present an overview of wind turbine technology

• To review the reliability characteristics of the main wind turbine components

• To summarize the different types of maintenance strategies and reliability concepts applicable

to wind turbines.

• To investigate the main technologies of condition monitoring and signal processing applied for

health monitoring of wind turbines.

• To overview the methods and models used for maintenance optimization.

This report should be arranged in such a way that all data is structurally presented in graphs, tables,

and lists with belonging descriptions and explanations in text.

The report should comply with the guidelines ofthe section. Details can be found on the website.

The mentor,

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Maintenance for wind turbines

Reliability analyses, health monitoring and maintenance

optimization

M.Dalmijn

41796226

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ii

Abstract

The purpose of this review is to give an overview of several topics related to WT maintenance. Studies aimed at reducing wind energy OM costs have gained much attention in recent years. This is justified by the high share of OM practices on the LCOE of wind energy. Before effective maintenance programs can be developed, there is the need for a reliability analysis of the concerned components. This implies that failure behaviour and expected lifetime of components as well as the causes and consequences of failure events should be identified. Over the years, multiple surveys containing failure data from several databases including MEP, WindStats, Swedish WTs, Reliawind and European offshore WTs have been published. The knowledge de-rived from these reliability studies can be used in order to employ some different types of maintenance, or a combination thereof. Traditionally, mostly corrective and scheduled maintenance have been applied to WT systems. However, in recent years much research has gone into condition-based strategies. These strategies offer several advantages when compared to conventional strategies and can reduce the OM costs. CMSs are required to enable the implementation of this maintenance strategy. The ultimate goal of CMSs is to provide a reliable indication about the presence, location and severity of a fault or to predict the future health condi-tions as well as the remaining useful life of components. Much research has been devoted to the development of effective techniques for health monitoring of WT components. Techniques for capturing fault related sig-nals including, SCADA systems, acoustic emission, fiber bragg gratings, vibration, etc, are discussed in this review. Additionally, this review addresses several signal processing techniques that are employed during multiple stages of the monitoring and diagnosis/prognosis process. The methods can be classified as using time domain, frequency domain, time-frequency analysis, model-based methods, probability-based meth-ods and artificial intelligence methmeth-ods. In contrast to diagnosis of WT faults, significant effort is still required to improve the prognostic capabilities of CMSs. Other advances can be made with techniques related to the storage and processing of the incredibly voluminous data sets generated by wind farms, the automation of alarm and decision systems, the employment of high resolution SCADA data and the cost-effectiveness and quality of sensors. Maintenance optimization enables the determination of the most effective and effi-cient maintenance plan. This is not easy since there are many different stochastic processes involved in OM of wind farms. Accurate modeling of all these processes is required to be able to analyze different mainte-nance strategies. The different options regarding models for damage, inspection, prognostics, weather, etc, are discussed. Furthermore, several techniques for solving optimization problems were presented. The vast majority of research in literature use simulation based methods. However, operation research models and analytical methods are also used. Most of the work in the area of maintenance optimization are in the the-oretical domain and do not incorporate prognostic capabilities of CMSs. Moreover, optimization is mostly performed with respect to one criterion. Therefore, efforts should be undertaken to make a shift towards real life applications, whilst including prognostic capabilities and multi-criteria optimization.

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4 Reliability analysis 15 4.1 Failure statistics . . . 15 4.1.1 WMEP . . . 15 4.1.2 WindStats . . . 15 4.1.3 Swedish WTs . . . 17 4.1.4 Reliawind . . . 17 4.1.5 European offshore WTs . . . 17 4.1.6 Other studies. . . 18 4.2 Failure characteristics . . . 18 4.2.1 Gearbox . . . 19 4.2.2 Blades . . . 20

4.2.3 Tower and foundation . . . 20

4.2.4 Pitch system . . . 20

4.2.5 Generator . . . 20

4.2.6 Power converter . . . 21 iii

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iv Contents

4.2.7 Braking system. . . 21

4.3 Future work . . . 21

5 Health monitoring 23 5.1 Data acquisition techniques . . . 24

5.1.1 SCADA systems . . . 24

5.1.2 Acoustic emission . . . 25

5.1.3 Fiber Brag gratings . . . 25

5.1.4 Vibration. . . 26

5.1.5 Shock pulse method . . . 26

5.1.6 Electrical effects . . . 26 5.1.7 Temperature measurement . . . 27 5.1.8 Oil analysis. . . 27 5.1.9 Ultrasonic testing . . . 28 5.1.10 Radiography . . . 28 5.1.11 Thermographics . . . 28

5.1.12 Overview per component . . . 28

5.2 Signal processing . . . 29 5.2.1 Time domain. . . 29 5.2.2 Frequency domain . . . 30 5.2.3 Time-frequency analysis . . . 30 5.2.4 Model-based methods . . . 30 5.2.5 Bayesian inference . . . 30

5.2.6 Artificial intelligence methods . . . 32

5.3 Future work . . . 32

5.3.1 Prognostic techniques . . . 32

5.3.2 High resolution SCADA data . . . 32

5.3.3 Big data . . . 32

5.3.4 Automation of alarm and decision systems . . . 33

5.3.5 Sensors . . . 33 6 Maintenance optimization 35 6.1 Optimization models . . . 35 6.1.1 Deterioration model . . . 35 6.1.2 Inspection . . . 36 6.1.3 Prognostics . . . 36 6.1.4 Weather model. . . 36 6.1.5 Maintenance actions. . . 37 6.1.6 Power model . . . 37 6.1.7 Design variables . . . 38 6.1.8 Cost model. . . 38

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Contents v

6.2 Optimization techniques . . . 38

6.2.1 Simulation . . . 38

6.2.2 Operations research models . . . 39

6.2.3 Analytical methods . . . 39 6.3 Future work . . . 39 6.3.1 Prognostics . . . 39 6.3.2 Case studies . . . 40 6.3.3 Multi-criteria analyses . . . 40 7 Conclusion 41 Bibliography 43

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1

Introduction

Sustainable energy generation systems are required in order to address the problems regarding global warm-ing and the limited fossil fuel reserves. The EU has set the target to raise the share of sustainable energy sources in energy generation to over 45% by 2030 [2]. In order to achieve this, the wind industry is rapidly expanding. In the Netherlands, an extensive program for the development of on- and offshore wind farms with an added capacity of 7GW is underway. These projects should be complete before 2023 [3]. The growing wind energy market has focused on the development towards larger wind farms and larger wind turbine (WT) rotor diameters in order to increase the cost effectiveness of WTs. Moreover, WTs are placed more remote and further offshore to access areas with higher wind speeds, and consequently, higher energy production.

One key factor in the development of new wind energy projects is their levelized cost of energy (LCOE). The LCOE, which includes capital cost, operation and maintenance (O&M) cost and the annual energy pro-duction, is used to measure lifetime costs divided by energy production. It enables cost comparison of dif-ferent technologies used for energy production [4]. A low LCOE strengthens the competitive position of wind energy and reduces its dependency on subsidies. It is estimated that the operation and maintenance costs for onshore wind farms comprise between 10 and 15% of the total LCOE [5]. Offshore wind farms contribute even more to the LCOE, with an estimated share of 15 to 30% [6, 7]. This difference might be explained by the harsh maritime conditions that accelerate the aging process of offshore WTs and poor accessibility for maintenance and repair during periods of strong wind and high waves.

Well developed maintenance programs can reduce the O&M costs of wind energy projects significantly. Cost reduction efforts of O&M will generally focus on both improving component reliability and reducing the cost to perform maintenance [5]. When developing a cost-effective maintenance strategy, two main aspects should be considered. First, there is the need for an accurate reliability analysis, so that the failure behaviour and expected lifetime of the WT components can be predicted accurately [8]. Second, an inspection method-ology should be devised which can be used to monitor the condition of the considered WT components. Leveraging both the reliability analysis and condition monitoring techniques, a maintenance strategy can be developed.

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2 1. Introduction

This survey aims to provide an extensive overview of maintenance for wind turbines. Although a wide variety of topics can be addressed within this research area, this review will focus on three main themes: reliability analyses, health monitoring and maintenance optimization. To be able to do so, five tasks are identified: i) to provide an overview of modern WT technology, ii) to summarize the different types of main-tenance strategies and reliability concepts applicable to WTs, iii) to review the reliability characteristics of the main wind turbine components, iv) to investigate the main technologies of condition monitoring and signal processing applied for health monitoring of WTs and, v) to overview the methods and models used for maintenance optimization. This discussion is focused on the horizontal axis, three-blade WT which is the dominant type of WT currently in operation in onshore and offshore wind farms.

This contribution is organized as follows. After the introduction, Chapter 2 will give a description of the used equipment for wind energy conversion. In Chapter 3, the main reliability and maintenance concepts ap-plicable to WT systems are explained. Several maintenance strategies are presented in this chapter. Chapter 4 will be devoted to a reliability analysis of WT components. Failure frequencies and corresponding down-times for the most important WT components originating from several databases are presented. Moreover, the dominant failure modes for the main components are discussed. Chapter 5 will present the methods and techniques used for health monitoring of WTs. The first part of this chapter will discuss the most common techniques for capturing fault related signals of WT components. In the second part, the main signal process-ing techniques used to diagnose or predict faults are addressed. In Chapter 6, models and techniques used for maintenance optimization are discussed. At last, this review closes with a summary and conclusion.

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2

Components of wind turbines

The main function of a WT is to transfer the wind’s kinetic energy into electricity. Within the category of wind energy conversion systems, there exist a great variety of WT designs. A typical WT will contain up to 8000 components [4]. This chapter will first discuss some of the different WT configurations that were installed in recent years. Furthermore, a detailed description of the most important WT components will be given.

2.1. Wind turbine configurations

Over the years, many different types of WTs have been deployed with each having its own characteristics. In general, WTs can be divided in two categories: horizontal axis and vertical axis turbines. Horizontal axis turbines have an axis of rotation that is parallel to the ground. In vertical axis turbines the axis of rotation is perpendicular to the ground. When determining the optimal number of blades for a WT, there are many factors that play a role including performance, loading, cost and noise [9]. However, it can be said that almost all conventional WTs have a 3-blade topology. Figure 2.1 depicts an overview of the typical WT components. Note that not all illustrated components are found in every type of WT.

Historically, there have been some different combinations of rotational speed, power control, drive train configuration and generator used within the horizontal axis WT as was shown by Pérez [10]. First, a distinc-tion can be made between constant and variable speed. In contrast to constant speed WTs, variable rotadistinc-tional speed WTs are capable of following the wind speed variations in order to maximize aerodynamic efficiency [11]. However, these systems require a costly electronic frequency converter for synchronizing the variable frequency power to the grid frequency. Power control is used to regulate the energy that is captured by the blades. Three types are distinguished: passive stall, active stall and active pitch control. The properties of these systems are further discussed in section 2.2.5. In indirect drive systems, a gearbox is placed between the main axle and the generator to increase the rotational speed of the generator input axle. Direct drive systems do not use a gearbox, but require larger and more expensive generators. Synchronous and induc-tion generators are used in WT systems. Figure 2.2 shows the development of WT configurainduc-tions in Germany between 1990 and 2008. The concerned WTs covered roughly 30% of all installed WTs in the world and can therefore be assumed to be a good representation of the overall population. It can be seen that the variable speed, pitch power controlled WT has become the standard. Over the years, double-fed induction generators

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4 2. Components of wind turbines

Figure 2.1: WT components: 1. foundation; 2. tower; 3. blades; 4. anemometer; 5. nacelle; 6. pitch system; 7. hub; 8. mean bearing; 9. low-speed shaft; 10. gearbox; 11. high-speed shaft; 12. brake system; 13. generator; 14. yaw bearing;

15. frequency converter; 16. mainframe/bedplate; [10]

and synchronous generators are taking over the share of induction generators until the preferred choice of selection was roughly equally distributed between the former two. The remainder of this section will provide a more detailed description of the main components that are used in WT systems.

Figure 2.2: Wind turbine configurations [10]

2.2. Main components

In this section the main WT components are discussed. Per component, the function and the most important design and maintenance considerations will be presented.

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2.2. Main components 5

2.2.1. Foundation and tower

Two main components of the WT structure are the foundation and the tower. The foundation provides stabil-ity to the entire WT system by transferring the generated loads to the ground. Several design options regarding the foundation type exists. For onshore WTs, shallow foundations such as octagonal gravity base foundations, rock anchor foundations, anchor cage foundations and mat foundations provide a feasible design option in cases of competent soil on surface level [12]. When dealing with weaker upper soil layers, deep foundations can be used to transfer the loads to a stronger layer. Monopile and multipile foundations are much chosen al-ternatives in these cases. For offshore WTs, foundation types including monopiles, gravity structures, tripiles and floating foundations can be used [12]. Water depth is a deciding factor in the choice of an offshore foun-dation [13]. The tower supports the nacelle and provides sufficient height for the blades to access high wind regions. WT towers are usually made of relatively thin-wall steel cylindrical elements. In recent years, alter-native tower designs including concrete towers, lattice structures and hybrid towers have been developed in order to increase the hub height of WTs [14]. The tower carries the vertical load from the nacelle and rotor. Aside from this, there is a horizontal load due to the wind, which results in a high bending moment at the bot-tom of the tower. One of the main considerations for tower design is the avoidance of resonant oscillations excited by rotor thrust fluctuations at rotational or blade passing frequency. The towers natural frequency should be as far away as possible from these frequencies in order to prevent unacceptably large stresses and deflections [9]. The main factors that determine the dimensions at the base of a circular steel tower are buck-ling of the shell wall in compression, strength under fatigue loading and stiffness requirements with respect to the natural frequency of the tower [9]. The tower should also provide access to the nacelle for maintenance activities. Increasing height of WT towers has led to the addition of lifts inside the tower, as climbing of the tower becomes physically more demanding.

2.2.2. Nacelle

The nacelle is placed on top of the tower and houses the main mechanical and electrical components such as the gearbox, generator, mechanical brake and frequency converter. The nacelle enclosure should protect these components from environmental factors such as wind, rain, salt and solid particles [15]. The nacelle is connected to the tower by means of a yaw bearing that enables the rotor to be pointed into the wind. A hydraulic or electrical yaw control system ensures that the correct position of the nacelle is maintained. Mechanical energy energy from the rotating blades enters the nacelle through the main shaft, which is sup-ported by the main bearing. From here, the rotor loads are transferred to the yaw bearing through the nacelle bedplate or mainframe. Sufficient accessibility of the components within the nacelle should be provided for maintenance activities. Moreover, the nacelle enclosure has to be openable for the removal of damaged components [15].

2.2.3. Gearbox

The gearbox has the function to increase the rotational speed from the rotor axle to speeds that are more suitable for operation of the generator. A typical induction generator for WTs at a rated power range between 300kW and 5MW operates at around 1500rpm. A gear ratio of approximately 1:31 and 1:125 is required to convert the rotational speed of the rotor, which lies at about 12 to 48 rpm for these WTs [9]. However, there are also systems that use smaller gear ratios. Note that these systems require a larger and more expensive generator as will be explained in Section 2.2.4. The most frequently applied gear types to WT main gears are spur, helical and planetary gears [15]. Most conventional WTs in the range of 1.5MW use a one- or a

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two-stage planetary gearbox [16], with ratios between 1:3 and 1:5 each. Due to persistent problems with gearbox reliability, typically found in WTs with high power ratings as a result of high loads, some alternative designs have been proposed [16]. Ragheb et al. [16] discuss some of these alternative options. They conclude that direct drive systems and torque splitting designs become more favorable for systems with higher power ratings, while continuously variable transmission (CVT), and magnetic bearing transmissions remain viable options for WTs in a lower power range. An adopted overview of the applicable gearbox options for different power ranges can be found in Figure 2.3

Figure 2.3: WT gearbox options for different power ratings [16]

2.2.4. Generator

Generators convert the mechanical power from the rotating shaft to electrical energy. With the disappearance of constant speed WT systems, three variable speed generator systems remain. Namely: doubly fed induction generators (DFIG), brushless generators with gear and full converter (GFC) systems and direct drive generator systems [17]. The main difference between DFIG and brushless generator with GFC systems is that the latter has a electronic converter for the full rated power, which enables better grid fault ride-through characteristics and to avoid the maintenance and the failures of the brushes [17]. Most conventional DFIGs contain 4 or 6 pole pairs, which require 1800 and 1500 revolutions per minute to be able to produce 50 or 60 Hz electrical power [16]. The last concept, the direct drive system, does not require the use of a gearbox. The reliability of these systems can be improved by eliminating this vulnerable component. Because of the low generator axle speed, direct drive systems require synchronous generators that contain much more pole pairs, which makes them large, heavy and expensive [17]. Moreover, high speed generators are less efficient than low speed generators [17].

2.2.5. Blades

WT blades transfer the kinetic energy of the wind into mechanical energy. Over the years, WT blades have become larger and larger. The longest WT blade today is fabricated by LM Wind Power and measures 88.4 meters [18]. Both the structural and aerodynamic design should be considered during the design process of a WT blade. The applied materials in WT blades have to meet a large set of mechanical, environmental and general requirements. Among mechanical requirements are a high stiffness, strength and fatigue resistance

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2.2. Main components 7

to ensure a stable operation. The blades suffer from heavy loads due to the wind speed and their own gravita-tional force. The latter has become more critical with the increase in turbine blade length. Furthermore, the blades have to withstand high fatigue loads associated with cyclic loading. Environmental aspects include lightning strikes, humidity and thermal stability. Some of the general requirements of blade materials are its cost and density. The large set of material requirements makes the selection of a material challenging. Currently, almost all blades are made from carbon and glass fibre materials [19]. The rotor hub connects the three blades to the main shaft.

As mentioned before, three types of power control are predominantly used in WTs: passive stall, active stall and active pitch control. Passive stall blades are the most simple form of power control. Here, the blades are designed to be inefficient at high wind speeds by increasing the generated drag and reducing lift. Active pitch systems rotate the entire blade to optimize the energy generation at changing wind speeds. Other ben-efits are the aerodynamic braking facility it provides and the reduced extreme loads on the turbine when shut down [9]. The pitch actuation system can be either individual for simultaneous control of the three blades. Hydraulic and electric mechanisms may be used to enable rotation of the pitch bearing. Active stall systems work similarly, however they rotate the blades in the opposite direction to that in active pitch systems. The main benefit of this is that only small changes of pitch angle are required to remain power output as rated. On the other hand, the behaviour of blades is much less predictable in stall regions.

2.2.6. Braking system

Braking systems are required as a form of over-speed control of WTs. Most braking systems apply frictional forces applied on the high-speed shaft in order to transfer the kinetic energy of the rotor into thermal energy [20]. Generally, furling and electromagnetic braking is first applied to reduce the shaft speed. This is required since the loads on the braking system at high shaft speeds will wear down the system rapidly [49]. Special alloys are used in braking systems as the braking disks experience temperatures of up to 700 °C [21]. Legisla-tion prescribes the use of two independent fail safe braking systems for WTs in most countries [21], because failure of the WT braking systems can have catastrophic consequences.

2.2.7. Control and electrical system

The function of the control system is to supervise the WT operation to optimize its performance, to maintain system safety and to report alarms in case some signal is above a set parameter limit value [22]. Johnson et al. [11] distinguish three levels of control for variable speed WTs. On the highest level, there is a supervisory controller that decides whether the wind speed is sufficient to start up the turbine or that the turbine has to be shut down due to high winds. The middle level, turbine control, has the goal of optimizing the energy generation under varying wind speeds. It comprises generator torque control, blade pitch control and yaw control. Generator torque control indirectly controls the rotor speed by opposing the the aerodynamic torque provided by the wind. As previously mentioned, pitch and yaw control both influence the aerodynamic ef-ficiency of the blades. The lowest level controller consists of internal generator, power electronics and pitch actuator controllers. These controllers have the function to realize the desired set values by the turbine-level control on a component level. The electrical system comprises of all the equipment required to deliver and control the electrical energy to the grid [22].

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2.3. Wind turbine component cost

The costs of the the different WT components are of importance to maintenance, because they make up a large part of the costs related to replacement and revision of components. As the previous description of the various WT configurations indicates, the costs of a WT of a specific type and size will vary. However, in Figure 2.4 a breakdown of component cost of a typical 5MW offshore turbine is depicted. It can be seen that the tower and rotor blades contribute most to the WT costs.

Figure 2.4: Distribution of the components costs for a typical 5MW offshore WT [4]

This chapter presented the main WT components. It is now necessary to study the reliability characteris-tics of all these components to determine the components that are most critical for WTs in operation. How-ever, before this can be done, there is the need for an explanation of the main reliability and maintenance concepts that play a role in WTs. This will be done in the next chapter.

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3

Reliability theory and maintenance

This chapter provides some theoretical background information on reliability and maintenance concepts that apply to WT systems. The concepts that are crucial for the development of effective maintenance strategies will be explained. This starts with the key aspects of reliability that are required for the reliability analysis of the WT system. Next, an overview of the most frequently used maintenance types will be given.

3.1. Reliability

Reliability is the the ability of an item to perform its required function under given conditions for a given time interval [22]. Poor reliability directly affects the profitability of WT project because it increases O&M costs and reduces availability to generate power [5]. A reliability study can be useful in areas of risk analysis, opti-mization of operations and maintenance. The risk analysis is a way of identifying causes and consequences of failure events, and the optimization is a way of telling how failures can be prevented and how to improve the availability of a system. One can see reliability theory as a tool for analyzing and improving the availability of the system [22].

3.1.1. Modelling system failures

The modelling of system failures comprises two parts. First, there is the need for an suitable statistical dis-tribution that will best fit the assessed failure characteristics. Second, parameter estimation methods are required to calibrate the parameters of the identified statistical distribution [23]. A much used tool for mod-elling of system failures are bathtub curves. They describe the development of a components failure rate over time. Three parts of the curve can be distinguished: burn-in, useful life and wear-out. Figure 3.1 illustrates a bathtub curve. Initially this curve is characterized by a high but decreasing failure rate due to infancy prob-lems (burn-in). With increasing age of the component the failure rate keeps decreasing until it reaches the second phase where the failure rate remains nearly constant (useful life). At the end of its life cycle a higher failure rate is found (wear-out), this indicates aging or wear-out effects.

Statistical distributions are used to fit failure data to the failure patterns described by the bathtub curve. For example, exponential distributions may be used to describe the constant failure rate during a components useful life. One important aspect of the exponential distribution is that it has no memory. The consequence of

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10 3. Reliability theory and maintenance

Figure 3.1: Bathtub curve (Spinato, [24])

this that an item is viewed as good as new as long as it is functioning. The normal and lognormal distribution can be used to fit to processes with increasing failure rates, related to the wear-out phase. However, the Weibull distribution remains the most versatile tool for modelling of failure data. With three different sets of parameters this distribution is able to fit all three phases of the Bathtub curve [22, 23]. Parameter estimation methods that are frequently applied include probability plot, regression analysis and Maximum Likelihood Estimation (MLE) [23].

3.1.2. Reliability performance

Availability is the reliability metric that is most important for wind farm O&M [25]. It plays a key role in wind energy projects, where a high availability is required in order to reduce the LCOE. In literature, several indices are used to describe the availability characteristics of wind turbine systems. The theoretical approach for determining the availability uses reliability indices: mean time between failure (MTBF) and the mean time to repair (MTTR). The MTBF is the average period between unplanned stoppages of the system. MTTR, is the mean time it takes to recover from failures. The availability can be described as a portion of operational time M T B F − MT T R, over a portion of total time. Alternatively, the ratio between the mean time to failure (MTTF), which is the average time of operation before failure occurs, and the MTBF can be used.

A =M T B F − MT T R M T B F =

M T T F

M T B F (3.1)

There are several ways to increase the availability of wind turbine systems. First of all, the component failure rate can be decreased. This improves reliability and increases the MTTF. A lower failure rate can be achieved by improving the design of a component. Alternatively, a component can be replaced before it fails. Another way to increase the availability of a wind turbine is by reducing the MTTR. Lastly, a reduction of the required maintenance time can also increase the up time of wind turbines.

3.2. Maintenance

Maintenance can be defined as the combination of all technical, administrative and managerial actions dur-ing the life time of an object in order to retain it in, or restore it to, a state in which it can perform its function

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3.2. Maintenance 11

[26]. The objectives of maintenance activities with respect to WTs are to: ensure system reliability, sustain operational safety and deploy the minimum resources required [27].

3.2.1. Maintenance classification

Maintenance classes can be divided into three groups:

• Corrective maintenance: a strategy aimed at restoring the equipment to its required function after it has failed [28].

• Preventive maintenance: a strategy intended to reduce the probability of failure or the degradation of functioning of an item by carrying out maintenance at predetermined intervals or according to pre-scribed criteria [29].

• Predictive maintenance: a preventive maintenance strategy that uses direct monitoring of the mechan-ical condition to determine the actual mean-time-to-failure of a system [30].

Figure 3.2: Maintenance classes

An overview of maintenance classes is depicted in Figure 3.2. The use of corrective maintenance requires the least amount of information about the system. Maintenance is simply carried out when components fail. If the failed component is critical for the WT operation, this will lead to unscheduled downtime, which is very costly [32]. Furthermore, there is the risk of consequential damage when failure of small components leads to a catastrophic system failure. Preventive maintenance is based on reliability characteristics of components. This knowledge is used to set the most suitable interval for a series of checks, replacements and/or compo-nent revisions for the periodic maintenance program [36, 37]. The goal of this maintenance policy is to reduce the failure frequency of the involved components [33]. This increases the availability of WTs and contributes to minimizing failure costs. Moreover, the time-based maintenance strategy allows scheduling of mainte-nance visits at periods of soft weather conditions, and thus avoids cancellations or postponing expeditions due to unexpected weather. Other benefits are a reduced spare parts stock and the possibility of grouping maintenance activities of multiple turbines in one expedition. On the other hand, this type of maintenance will inherently lead to unused lifetime of components as it is impossible to make a perfect estimation. As a result, the cost of maintenance will increase. In summary, it can be said that preventive maintenance offers uptime at the cost of unused lifetime of components. In terms of technical requirements, predictive main-tenance or condition-based mainmain-tenance (CBM) is most demanding. A condition monitoring system (CMS) is required in order to gather information about the condition of the system. Performance and parameter

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12 3. Reliability theory and maintenance

monitoring may be scheduled on request or continuously [31]. A detailed review of this subject will be given in Chapter 5. The CMS will be accompanied with additional cost. On the other hand, predictive maintenance offers many advantages over traditional maintenance strategies. First of all, it reduces labour and material costs because maintenance is only performed components reach their end of life. Secondly, maintenance activities can be scheduled more efficiently, and thus further reduce maintenance cost. Thirdly, the CMS can identify failing components in an early stage, when only minor maintenance is required. Overlooking failure in an early stage can cause a minor failure to become a catastrophic failure, leading to high repair costs and long downtime. Another factor that strengthens the case for predictive maintenance is that the used CMS can applied be to extend the lifetime of WTs if the monitoring system is able to judge that the WT is mechanically and structurally sound.

3.2.2. Opportunistic Maintenance

Opportunistic maintenance can be seen as a combination of corrective and preventive maintenance. In case of a failure in a WT, this opportunity can be used in order to perform preventive maintenance on other com-ponents. Both positive and negative economic dependencies should be considered for optimization of an opportunistic program [34]. The positive dependence indicates that combining maintenance actions can lead to cost savings when compared to separate maintenance [127]. The negative dependence occurs when maintaining components simultaneously is more expensive than maintaining components individually. This can be the case when the advancement of maintenance activities leads to the reduction of components’ use-ful life [35]. The decision whether or not to maintain the other components depends on a decision making criterion; e.g. component age [127], long term cost or reliability probability threshold [35].

3.2.3. Reliability centered maintenance

Reliability centered maintenance (RCM) is a much used approach for optimizing preventive maintenance activities that was developed in the aviation industry and later adopted to many other industries [39]. RCM defined as “. . . .a systematic consideration of system functions, the way functions can fail, and a priority-based consideration of safety and economics that identifies applicable and effective preventive maintenance tasks” [40]. The main objective of RCM is to reduce the maintenance cost, by focusing on the most important functions of the system, and avoiding or removing maintenance actions that are not strictly necessary [40]. The approach focuses on the functions of equipment in order to predict failure modes and the resultant consequences so that suitable maintenance actions can be determined [41]. The implementation of RCM is build upon answering the following seven questions [39]:

• What are the functions and associated desired standards of performance of the asset in its present operating context (functions)?

• In what ways can it fail to fulfill its functions (functional failures)? • What causes each functional failure (failure modes)?

• What happens when each failure occurs (failure effects)? • In what way does each failure matter (failure consequences)?

• What should be done to predict or prevent each failure (proactive tasks and task intervals)? • What should be done if a suitable proactive task cannot be found (default actions)?

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3.2. Maintenance 13

In this chapter some of the key maintenance strategies and reliability concepts applicable to WT systems were presented. This contribution can now proceed to review the literature on reliability analyses of wind energy equipment. This will be done according to the reliability concepts presented in this chapter; e.g. failure rate, downtime, availability and failure modelling.

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4

Reliability analysis

Failure statistics are an important tool in order to asses the reliability performance of WTs. Failure rates and corresponding downtimes can give insight in the availability of a specific WT design. Failure models can be fitted to data in order to interpret failure behaviour. These insights are crucial for the design of preventive and predictive maintenance strategies. Knowledge of the failure modes related to component failure are an important requirement for the design of CMS systems used in CBM. Moreover, failure statistics can confirm the design lifetime of a WT or it can indicate that the intended lifetime is not achieved. In these cases, analysis of failure may result in a redesign of a component. This chapter will first review the literature on failure statistics of different databases. Later, failure characteristics of WT components are discussed.

4.1. Failure statistics

Several surveys covering failure statistics from multiple databases have been published. The used data origi-nates from databases including WMEP, WindStats, Swedish WTs, Reliawind and European offshore WTs. The main conclusions from these studies are discussed in this section.

4.1.1. WMEP

A survey that monitored the operation of 1500 mostly sub-MW WTs during a period of 15 years (1989-2004), found that an average availability of 98 percent was obtained [44]. The failure rates of the WTs show a declin-ing development in the first years of operation. A higher failure rate is found for WTs in higher power classes. The majority of malfunctions are observed in the plant control system and the electrical system. However, the downtime succeeding failures was highest for the gearbox and generator, shortly followed by the drive train. Figure 4.1 shows an overview of the found failure rates and corresponding downtimes.

4.1.2. WindStats

In Tavner et al. [43], the failure statistics originating from the Windstats database concerning 4500 turbines installed in Germany and 2500 turbines installed in Denmark between 1994 and 2004 is analyzed. The tur-bine size ranges from 100kW up to 2.5MW. In the German population, the main contributors to failures were

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16 4. Reliability analysis

Figure 4.1: WMEP component failure rates and corresponding downtimes [44]

found in the grid or electrical system. In the Danish population, the yaw system was prone to most failure. A higher failure rate was found in the German population, this was explained by their lower average age, which causes them to be in the early failure region of the bathtub curve. Furthermore, the German WT’s are larger and house variable speed technology. This study presents no new data on the consequential downtime suc-ceeding failure. Another study by Tavner et al. [45], analyses the data from the Windstats database in order to model life curves of the turbines. A decreasing failure rate over time for both German and Danish WTs is found which is clear evidence of infant mortality effects. It is widely accepted that Power Law Process (PLP) model is most appropriate to fit this type of data [8]. The PLP model was used in order to approximate the Weibull distribution in the infant mortality stage for both German and Danish turbines. The results are de-picted in Figure 4.2. Although the population of analyzed WT’s is very large, the age and power rating raise questions whether this survey is representative for modern WT’s. Spinato et al. [24] employs the PLP in order to asses the reliability of different sub-assemblies of WTs. The PLP interpolation of data is given for several generators, gearboxes and converters.

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4.1. Failure statistics 17

4.1.3. Swedish WTs

The Royal Institute of Technology of Stockholm carried out a research aimed at analyzing the reliability of Swedish WTs. Failure data originating from a varying number of wind power systems in Sweden in the period between 1997-2005 was collected. Ribrant [22] reports the found failure frequency and the distribution of downtime per component between 2000-2004. Most failures were found in the electrical system followed by sensors and blades/pitch components. An overview can be found in Figure 4.3. The main contributors to downtime are gearboxes closely followed by the control system and the electric system. The full percentage breakdown can be seen in Figure 4.4.

Figure 4.3: Distribution of failures per component for Swedish Wts [22]

4.1.4. Reliawind

A more recent survey (2011) categorizes more than 35000 downtime events from 350 WTs from the Reliawind project [46]. This project only contains modern WT’s with variable speed, pitch regulation and a rated power of at least 850kW that have been running for at least two years. The highest failure rates are found in the pitch system followed by the yaw system and frequency converter. An overview of the contribution of every component to the overall failure rate can be found in Figure 4.5. Furthermore, the contribution on overall downtime per component was analyzed. This is visualized in Figure 4.6 and shows that the pitch system, frequency converter and generator contribute most to downtime in following order.

4.1.5. European offshore WTs

A recent study (2015) of Carrol et al. [42] is based on approximately 350 offshore WTs from a leading manufac-turer. This data comprises a total of 1768 turbine years originating from WTs that were between 3 and 10 years old at the time of the publication. Due to confidentiality reasons no exact data can be given about the type of WT’s under consideration. However, it can be said that the survey covers modern WT’s with a nominal power between 2 and 4MW, spread over 5 to 10 wind farms in Europe. The highest failure rate is found in the pitch

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Figure 4.4: Distribution of downtime per component for Swedish Wts [22]

and hydraulic systems. The ’other components’ class is the second biggest contributor, followed by the gen-erator. This review also presents data on the average repair time (unlike downtime it does not include travel time, lead time, time added on because of inaccessibility and so on.), repair cost and number of technicians required for repair. In all these categories the hub, blades and gearbox are among the top three contributors. Furthermore, the study shows a stronger correlation between increasing average wind speeds and increasing average failure rates in offshore wind farms compared to onshore wind farms. It finds an average failure rate of about 10 failures per turbine per year by the third operational year. No clear resemblance with the bathtub curve was found when analyzing the failure data of the components all together. The reason for this was that some components with high failure rates, such as the pitch and hydraulic system, do not follow the bathtub curve. On the other hand, components including the converter and electrical components, do show failure behaviour that follows the bathtub curve.

4.1.6. Other studies

A survey by Pérez et al. [10] reports on average failure rates found in different studies considering European wind farms. It finds that the control system, blades/pitch system and electric system are the main contrib-utors to failure. Some other studies present data on average availability of wind farms, but lack information about the rates of failure for specific components. In [48] the trends related to the availability operating wind farms are analyzed. On average, an availability of about 97% can be expected. The availability is relatively incentive to turbine size and wind farm size. Higher wind speeds reduce availability.

4.2. Failure characteristics

This section will provide an overview of failure characteristics like failure causes and failure modes of the major WT components.

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4.2. Failure characteristics 19

Figure 4.5: Contribution of components to overall failure rate in the Reliawind database [46]

Figure 4.6: Contribution of components to overall downtime in the Reliawind database[46]

4.2.1. Gearbox

Gearboxes have been a component of specific concern. Because gearboxes operate under poor working con-ditions including heavy load, wind gusts, or dust corrosion, failures of the wind turbine gearbox are various [49]. Gearboxes fail to meet their expected lifetimes and are costly and hard to replace. Therefore, the National Renewable Energy Laboratory (NREL) has started a special program, the Gearbox Reliability Collaborative (GRC), to get an understanding of the causes of this problem. A database containing 1050 failure incidents show that gearboxes fail in drastically different ways [50]. An analysis of the database shows that roughly 76% of failure occurs in bearings, 17% in gears and 7% in other systems. Typical gearbox faults include shaft im-balance, shaft misalignment, shaft damage, bearing damage, gear damage, broken shaft, leaking oil, high oil temperature and poor lubrication [49] as well as oil contamination due to defective sealing and wear or fa-tigue damage [56]. Sheng [51] analyzed data from the GRC database and concluded that the top gear bearing failure modes include: hardening cracks, abrasion (scratching of surfaces), adhesion (scuffing, welding and tearing of materials). The top failure modes found in gears are: fretting corrosion and high-cycle bending

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fatigue.

4.2.2. Blades

Wind turbine blades amount for 15-20% of the wind turbine costs [52]. It has also been shown that blades are among the highest contributors in downtime. Moreover, minor blade damage can lead to a mass unbalance in the entire blade, which in its turn can cause catastrophic blade failure. These factors stress the importance of the reliability of this subsystem, and thus extensive studies have been performed to find the causes of blade failure [53]. There are many causes for WT blade failure including manufacturing defects, lightning, poor design [54], wind gusts, moisture absorption [55] and damage during transportation [51]. Failure modes tend to differ from one design to another. However, faults are predominantly related to strength and fatigue of the composite materials [56]. Among the defects are cracks, erosion, debonding, separation and delamination of the composite material as well as UV effects on the fibers and web failure [54].

4.2.3. Tower and foundation

Structural failures in the tower and foundation are mainly caused by extreme wind speeds and distribution, extreme turbulences, maximum flow inclination and terrain complexity [58], and also ice accumulation, hail, bird strikes, dust particle impacts, or lightning strikes [56]. These factors can lead to non-catastrophic defects like cracks in the concrete base, corrosion, gaps in the foundation section, loosen studs joining the foundation and the first section, loosen bolts joining sections and welding damages [56]. Very rarely failure manifests itself in the form of foundation collapse [59] or tower buckling.

4.2.4. Pitch system

Multiple studies find that the pitch system has the highest overall rate of failure in the WT system. An impor-tant cause for failure of pitch systems is turbulence of the wind [57]. Some common problems found in pitch bearings include ellipse truncation, cage wear and surface fatigue, cracks originating from stress concentra-tions and quality issues from poor induction hardening [60].

4.2.5. Generator

Generator faults mainly include mechanical failures, electrical failures and cooling system failures [61]. Me-chanical failures typically arise in the generator bearings and to a lesser extend, in the generator rotor and stator [62]. Among causes of bearing failure are fatigue cracks, asymmetry, imbalance [63] and instability of the oil film [61]. The origin of rotor and stator failures lie in broken bars, air-gap eccentricities, dynamic rotor eccentricities [56], rotor crack and loosening socket [61]. Rotor imbalance can have its origin in ice and dirt accumulation on the blades. Electrical failures can arise in the rotor and stator windings in the form of elec-trical breakdowns [64]. This can be caused by thermal degradation, vibration and switching pulses leading to mechanical stress and stress caused by the difference in thermal expansion coefficient of the materials [65]. Long exposure times to high oil temperature can also cause damage of the generator. The causes of cooling system failure include jams in the oil cycle, oil leaks, defective pipelines and oil deterioration [61]. Alewine and Chen [62] studied failure in about 1200 generators from different manufacturers. They provide data on the share of sub-component level failures in generators. This is visualized in figure 4.7. It can be seen that the vast majority of failures occurs in the generator bearings.

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4.3. Future work 21

Figure 4.7: Share of sub-component level failure in generators [62]

4.2.6. Power converter

Shipurar et al. [64] identify the most failing components in power converters. These are: the power semi-conductors, the control, the passives, conductor boards and fuses. Solder joint fatigue and bond wire lift off are two of the main failure mechanisms in power semiconductors. Mechanical stress due to difference in coefficients of thermal expansions are the cause of these mechanisms. Failure can occur when the power converter undergoes many temperature cycles. Moreover, these cycles can also cause failure in the control unit. Voltage stress and temperature stress leading to parameter degradation are the most common causes for failure in capacitors.

4.2.7. Braking system

Wear of braking pads, resulting in a lower braking force is one of the most common failure modes [61]. Various hydraulic faults including leaking oil, contaminated oil and air mixing in the hydraulic system can cause malfunctioning of the brakes [61]. Motor faults and power line faults are other typical braking system failures that can occur [21].

4.3. Future work

Reluctance of turbine manufacturers to release performance data has led to a lack of available reliability anal-yses in the public domain [42]. Additionally, most of the available studies do not cover modern WTs. This raises questions whether they are representative for operational wind farms. For these reasons, there is the need for more recent reliability analyses in the public domain which cover data originating from modern WTs. Another issue with the available sources is that the taxonomies of the WTs under consideration differ widely and no consequent documentation is used. Due to these factors, the results of the reliability studies are not easily compared. Tavner et al. [45] advise that failure codes and names and descriptions of different sub-assemblies should be homogenized in order to make comparison between studies more valuable.

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The results of the reliability analyses (e.g. failure rate and downtime) presented in this chapter are key to the identification of the components that contribute most to system unavailability. Moreover, the failure characteristics such as failure causes and failure modes provide crucial knowledge about the failure behaviour of the concerned components. These factors determine what components should be monitored and what signals could be captured in order to do so. The next chapter is devoted to a discussion of data acquisition and signal processing techniques that can be used for health monitoring of WT components.

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5

Health monitoring

Health monitoring (HM) refers to the process of using a sensing system to detect damage to an object, with damage being defined as a change in the object’s properties that adversely affects current or future perfor-mance [66]. Within the subject of health monitoring, two types of monitoring can be distinguished: structural health monitoring (SHM) and condition monitoring (CM). SHM aims to monitor the structural elements of the WT such as the blades, foundation and tower. CM is health monitoring for rotating or reciprocating ma-chinery [13]. Typically, a much higher sampling frequency is required for this type of monitoring. These two terms are often used synonymously for quite different topics. One cause of the confusion might be that both SHM and CM are used for the same goal, but cover different kinds of monitoring objects [67]. Condition mon-itoring systems (CMS) can be used to provide continuous indications component conditions [68]. As a result, maintenance tasks can be scheduled more efficiently, leading to increased reliability, availability, maintain-ability and safety whilst downtime, maintenance and operational costs are reduced [69]. In this review, when we speak of CMS it can include systems for both SHM and CM.

Figure 5.1: Overview of CM and maintenance processes [79]

Within the HM process, two objectives can be distinguished: fault diagnosis and fault prognosis. In the former, the ultimate goal is to provide a reliable indication about the presence, location and severity of a fault

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24 5. Health monitoring

in a WT [70]. Fault prognosis is aimed at predicting the equipment future health conditions as well as the remaining useful life [105]. The role of CMSs within the application of maintenance strategies is depicted in Figure 5.1. Three stages are distinguished within the CM and SHM processes: data acquisition, signal con-ditioning and feature extraction. First, a network of sensors is required for the collection of measurements. These sensors should be able to capture fault related information from the components under consideration. Signal conditioning may be applied in order to emphasize the valuable information that a signal contains. Among signal conditioning methods are basic operations including amplification, filtering, linearization and modulation/demodulation [75]. The next step is to extract features from the signal that are related to the health of the component. These valuable features can be used for diagnosis as well as prognosis of WT faults. According to the obtained information, different maintenance strategies can be employed. Some studies sur-vey the CMS systems based on SCADA [71] and CM [72] that are currently on the market. The remainder of this chapter is divided into two sections. First, the most important techniques for capturing signals for CMSs will be discussed. The second part addresses the signal processing techniques that are employed in the subsequent stages.

5.1. Data acquisition techniques

Over the years, multiple surveys have been published that aim to report the different condition monitoring techniques [53, 68, 73–76]. The techniques used in CMS can be divided into two categories: offline monitor-ing and online monitormonitor-ing. Offline monitormonitor-ing requires the deployment of maintenance personnel. The WT under consideration needs to be shut down during these monitoring activities [68]. On the other hand, online monitoring can be performed in-service. Moreover, it provides deeper insight in the components health state when compared to offline inspection. A last advantage is that these systems can trigger alarms automatically, which is essential for unattended WT operation, particularly in remote or inaccessible locations [68]. This section will provide an overview of the techniques used to capture signals for CMSs.

5.1.1. SCADA systems

This type of monitoring was traditionally used to confirm the WT operation and to measure energy produc-tion. SCADA systems transmit 5-10 minute averaged values of several parameters like [78]:

• Active power output

• Anemometer-measured wind speed • Nacelle temperature

• Gearbox bearing temperature • Gearbox lubricant oil temperature • Generator winding

• Power factor • Reactive power • Phase currents

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5.1. Data acquisition techniques 25

In most recent wind farms, SCADA systems are a standard installation [79]. This is also one of the main advantages of these systems, as no additional hardware investment is required [76]. This makes SCADA based systems cheap compared to other CMSs. However, there are also some concerns with respect to the use of SCADA data for health monitoring purposes. Firstly, conventional processing techniques cannot be applied to interpret SCADA data, caused by their low sampling rate. Secondly, SCADA measurements do not collect the required information for a full CM of all components of a WT. Lastly, the variation of SCADA data under a wide range of environmental conditions makes it hard to detect faults without data analysis tools [76]. Some of the issues related to the low frequency resolution of most SCADA systems may be solved by implementing high resolution SCADA systems. These systems are more capable of capturing the dynamic behaviour of the turbine and thus improve detection capabilities, as was recently shown by Gonzalez et al. [80].

5.1.2. Acoustic emission

Acoustic waves are emitted when materials undergo a change in their internal structure. Processes like, crack-ing, deformation and debonding all release a certain amount of elastic energy, which propagate as elastic waves. By capturing and analyzing the waves, damage in components can be detected and localized. The AE sensors, mounted on the surface of the material in which the waves propagate, are used to capture the sound waves. By means of registrating these waves, mostly piezoelectric sensors within a frequency range of 100kHz to 1Mhz are used, but other research also focuses on fiber optic sensors and other transduction methods [77]. The captured signal is filtered and then amplified 100 to 1000 times in order to extract the valu-able information. AE signals have a high signal to noise ratio which makes them suitvalu-able for application in high noise environments [68]. Moreover, AE can be applied to for the early detection of failure. On the other hand, AE requires costly data acquisition and processing equipment due to the high sampling frequencies related to this monitoring technique [74]. AE have been applied for damage detection in blades, gearboxes and bearings.

For example, a recent laboratory study by Tang et al [81], investigated the possibility of in-service moni-toring of blades by acoustic emissions. A 45.7m long, 2MW WT blade was subject to cyclic loading by compact resonant masses to simulate in-service load conditions. Before the final eight days of the experiment, a defect of dimensions 1.00 x 0.05 x 0.01 was introduced to the blade. The four AE sensors discovered fatigue damage that propagated from the initial defect. Furthermore, analyses of the relative arrival times of the damage sig-nals, successfully determined the location of the damage growth. This AE monitoring methodology used in this study shows promising results for in-service monitoring of blades.

5.1.3. Fiber Brag gratings

Fiber bragg gratings (FBG) are a form of fiber optic sensors that can be used for strain measurements. Within the core of a fibre, a bragg reflector ensures that particular wavelengths are reflected while transmitting oth-ers. The central wavelength of the spectrum that a bragg grating reflects is called the bragg wavelenght and is dependent on the grating period. This grating period is altered when subjected to axial strain, causing a pro-portional shift in the bragg wavelength. Following one time calibration, strain values can be measured. One advantage of FBGs are that multiple bragg gratings can be fabricated along the same fibre, allowing a network of stain sensors in a single fibre. Furthermore, strain based techniques like FBGs can be operated at lower sampling rates compared to vibration-, AE- and electrical-signal-based techniques and they do not require power sources [68]. In order to use the FBG sensors, they must be simple adhered to or embedded in the material in which measurements are required. The application of fiber optic strain sensors has been mainly

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26 5. Health monitoring

studied for WT blades. For example, Schroeder et al. [82], proved the in-service capability and reliability of fibre optic sensing technology. A FBG measurement system has been installed in a 4.5MW WT that enabled continuous load monitoring for two years, of which about one year at 4Hz and one year at 50Hz. The data was used for safety monitoring and characterization of the loads during turbine operation. However, no diagnos-tic system that was able to detect failure in the blades was applied. The possibility of damage detection using a FBG sensing network was proven by Sierra et al. [83]. Here, four optical fibres, with each six FBG’s, were used for monitoring a 13.5m turbine blade. Several static test were conducted, including test with artificially inflicted defects.

5.1.4. Vibration

Vibration analysis has been one of the most popular monitoring techniques for rotating parts, such as gear-boxes and bearings. Vibration characteristics like stiffness or damping will be changed by a loss in structural integrity. Measurements in different frequency ranges require different sensors. The most widely used vibra-tion sensor in WT CMS systems are accelerometers [68]. This is due to their wide working bandwidth, which ranges from 1 to 30 kHz. Damage identification methods using vibration measurements can be categorized in four groups: natural frequency-based methods, mode based methods, curvature/strain mode shape-based methods and methods using both mode shapes and frequencies [84]. One of the main difficulties in vibration analysis is to be able to distinguish vibration signals that originate from defects and vibration from normal usage. Furthermore, modal parameters are not very sensitive to damage [85].

Tcherniak and Mølgaard [86] introduce a alternative technique for vibration analysis of WT blades. It uses an electromechanical actuator in order to introduce vibrations in the blade. A frequency of about 1kHz is used which ensures longer wave propagation and a higher resolution for damage detection. The vibra-tions are picked up by several accelerometers placed along the structure of the blade. This technique was first tested in a laboratory setup where it proved to be able to detect damage. In another study, damage lo-calization was also proven to be possible [88]. Aside from static testing, the monitoring system was tested on an operational Vestas V27 WT [87]. The system was able to detect damage up to a resolution of 15cm under different environmental conditions.

5.1.5. Shock pulse method

The shock pulse method (SPM) is a technique for CM of rolling bearings. The SPM is based on the idea that a ball or roller in a bearing that comes into contact with a damaged area produces mechanical shock waves. The mechanical shock waves typically lie in the frequency range of 35-40kHz, which are well above other resonant frequencies [89]. These frequencies can be detected by a piezoelectric transducer mounted on the bearing housing that has a resonant frequency that lies in the range of the mechanical shock waves. Therefore, the transducer effectively works as a bandpass filter as only the shock pulses will trigger the resonant frequency of the piezoelectric crystals [90]. One major benefit of the SPM when compared to vibration analysis is that the SPM is not affected by machine component resonances.

5.1.6. Electrical effects

CM of electrical equipment such as motors, generators and accumulators is typically performed using voltage and current analysis looking for an unusual phenomenon [75]. For example, an in-depth study is performed in order to analyze the influence of rotor electrical asymmetry on the stator line current and total instan-taneous power spectra of WT induction generators (IG) [91]. This work identifies a number of fault related

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5.1. Data acquisition techniques 27

frequencies in stator current or total power spectra. Subsequently, an algorithm was developed in order to track these frequencies under variable WT speed conditions. Tests in an experimental setup showed that this algorithm was capable of reliably detecting IG rotor electrical faults. In addition to electrical faults, analysis of electrical effects can be used to detect fault induced vibrations by other components in the generator shaft. For example, analysis of generator current signals can be used to detect torsional vibrations due to gearbox and bearing faults. Wang et al. [92] propose a system where the rotor and stator current is used to provide in-formation on the shaft speed for order tracking of the vibration signal. Bearing faults induce a characteristic vibration in the shaft which can be extracted with this method. This technique is especially useful for shafts that operate at varying speeds, because the smearing problem. A recent study by Cheng et al. [93] analyze the generator stator current to predict the RUL of a WT gearbox. The actual failure time can be predicted accu-rately when the RUL is only at 32.7% of its lifetime. Rotor eccentricity can also be detected by analyzing the stator current characteristic frequencies of the excitation generated by a WT generator [94]. The use of elec-trical signals for health monitoring offer two advantages compared to other monitoring technologies. First, no additional sensors are required since WT control and protection systems already use electrical signals and second, the used signals are reliable and easily accessible [].

5.1.7. Temperature measurement

An exceeding of the normal operational temperature threshold can be a sign of ongoing deterioration pro-cesses such as excessive mechanical friction due to faulty bearings and gears, insufficient lubricant proper-ties, and loose or bad electrical connections [95]. Therefore, temperature measurement can be used to moni-tor the health of WT components including gearboxes, generamoni-tors, bearings, and power converters [68]. Tem-perature measurement is considered to be a cost-effective and reliable monitoring technique. However, two main problems with temperature measurement have been identified by Qiao and Lu [68]. First, the source and root cause of the measured temperature rise is not always clear. Temperature variation of a nearby com-ponent can also cause a shift in the measured temperature. Second, the temperature-based methods require the installation of embedded thermal sensors, which are intrusive to the system being monitored and are quite fragile in a harsh environment.

5.1.8. Oil analysis

The main goal of lubrication oil is to provide a continuous layer of film between surfaces in relative motion to reduce friction and prevent wear, and thereby, prevent seizure of the mating parts [96]. Analysis of the oil quality in lubrication systems should be used to determine whether the oil can still perform its function and to help detect failure symptoms in an early stage. Parameters such as viscosity, water content, level, dielectric constant (DC), magnetic susceptibility, particle counting and identification, temperature and pressure are can be monitored [98, 99]. It is known that oil contamination, oil degradation and the lubrication systems parameter change can precede failure of components [99]. In comparison with vibration based monitoring, warnings for defects can be provided ten times earlier in oil based monitoring [97]. Currently, oil analysis is mostly executed offline [73]. Sampling of lubrication oil takes place roughly every six months and analysis takes place off-site [98]. On the other hand, some researches focus on the implementation of online lubrica-tion monitoring systems [98–100]. One of the main limitalubrica-tions with respect to oil analysis is that oil analysis can not be used to detect defect outside the mechanical component it is used in. Furthermore, the required equipment for online monitoring is highly expensive [79].